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J-MoDL: Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction
IEEE Journal of Selected Topics in Signal Processing ( IF 7.5 ) Pub Date : 2020-10-01 , DOI: 10.1109/jstsp.2020.3004094
Hemant Kumar Aggarwal 1 , Mathews Jacob 1
Affiliation  

Modern MRI schemes, which rely on compressed sensing or deep learning algorithms to recover MRI data from undersampled multichannel Fourier measurements, are widely used to reduce the scan time. The image quality of these approaches is heavily dependent on the sampling pattern. In this article, we introduce a continuous strategy to optimize the sampling pattern and the network parameters jointly. We use a multichannel forward model, consisting of a non-uniform Fourier transform with continuously defined sampling locations, to realize the data consistency block within a model-based deep learning image reconstruction scheme. This approach facilitates the joint and continuous optimization of the sampling pattern and the CNN parameters to improve image quality. We observe that the joint optimization of the sampling patterns and the reconstruction module significantly improves the performance of most deep learning reconstruction algorithms. The source code of the proposed joint learning framework is available at https://github.com/hkaggarwal/J-MoDL.

中文翻译:

J-MoDL:用于优化采样和重建的基于联合模型的深度学习

现代 MRI 方案依靠压缩感知或深度学习算法从欠采样的多通道傅立叶测量中恢复 MRI 数据,被广泛用于减少扫描时间。这些方法的图像质量在很大程度上取决于采样模式。在本文中,我们介绍了一种连续策略来联合优化采样模式和网络参数。我们使用多通道前向模型,由具有连续定义采样位置的非均匀傅立叶变换组成,在基于模型的深度学习图像重建方案中实现数据一致性块。这种方法有利于采样模式和 CNN 参数的联合和连续优化,以提高图像质量。我们观察到采样模式和重建模块的联合优化显着提高了大多数深度学习重建算法的性能。提议的联合学习框架的源代码可在 https://github.com/hkaggarwal/J-MoDL 获得。
更新日期:2020-10-01
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